{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05e35b6e",
   "metadata": {},
   "source": [
    "# Overview of the dataset\n",
    "Reading and visualizing a single sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ebf6ef4",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#imports and global variables\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "import pandas as pd\n",
    "import random\n",
    "from scipy import stats\n",
    "from scipy.signal import find_peaks\n",
    "import json\n",
    "samples = 219\n",
    "\n",
    "data_path = './Dataset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6eb8c30d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample User Characteristics:\n",
      "User Id: 54\n",
      "User Age: 62\n",
      "User Occupation: Administrative Assistent\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: In a future version of pandas all arguments of read_csv except for the argument 'filepath_or_buffer' will be keyword-only\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read all files for random sample and print info\n",
    "a = random.randint(1,samples)\n",
    "sample_path = os.path.join(data_path,str(a))\n",
    "json_path = os.path.join(sample_path,str(a)+'.json')\n",
    "#read data and visualize\n",
    "with open(json_path, 'r') as f:\n",
    "    sample_data =  json.load(f)\n",
    "    user_id = sample_data['ID']\n",
    "    user_age = sample_data['Age']\n",
    "    user_weight = sample_data['Weight']\n",
    "    user_height = sample_data['Height']\n",
    "    user_education = sample_data['Education']\n",
    "    user_occupation = sample_data['Occupation']\n",
    "    user_region = sample_data['Region']\n",
    "    user_covid = sample_data['COVID-19_vaccinated']\n",
    "    user_gymfreq= sample_data['Gym_frequency']\n",
    "    user_sport = sample_data['Sport_practice']\n",
    "    user_freqexerc = sample_data['Frequency_of_exercise']\n",
    "    user_exercsdur = sample_data['Exercise_session_duration']\n",
    "    user_prefexrc = sample_data['Prefered_exercises']\n",
    "    user_specdiet = sample_data['Specific_diet']\n",
    "    user_fruitsveg = sample_data['Fruits-vegetables_per_day']\n",
    "    user_junkfood = sample_data['Candy-Salty_snacks-soda_consumption']\n",
    "    user_wcoms = sample_data['Water_consumption']\n",
    "    user_hprobl = sample_data['Health_problems']\n",
    "    user_dmed = sample_data['Daily_medication']\n",
    "    user_shab = sample_data['Smoking_habits']\n",
    "    user_shours = sample_data['Sleeping_hours']\n",
    "    user_abc = sample_data['Alcoholic_beverage_consumption']\n",
    "    sensors = sample_data['sensors']['name']\n",
    "    print(\"Sample User Characteristics:\")\n",
    "    print(\"User Id:\",user_id)\n",
    "    print(\"User Age:\",user_age)\n",
    "    print(\"User Occupation:\",user_occupation)\n",
    "    df = pd.read_csv(os.path.join(sample_path,str(a)+\".txt\"),'\\t', skiprows=3)\n",
    "    if(len(df.columns)>1):\n",
    "        df = df.iloc[:,5:6]\n",
    "    graf = df.plot(title='ECG', figsize=(30,5))\n",
    "    graf.legend(['ECG'])\n",
    "    df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "514daab6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# import xgboost as xgb\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec528a4",
   "metadata": {},
   "source": [
    "# Classification Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b6e3206",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genderfile_csv = os.path.join(data_path, 'TabelaGender.csv')\n",
    "df_gender = pd.read_csv(genderfile_csv)\n",
    "df_gender.columns = df_gender.columns.str.replace(\"Average of \", \"\")\n",
    "df_gender.drop('ID', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dc80c7",
   "metadata": {},
   "source": [
    "Classification on gender features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db7445aa",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "train, test = train_test_split(df_gender, test_size=0.3)\n",
    "\n",
    "X_train = train.iloc[:,1:]\n",
    "y_train = train.iloc[:,0]\n",
    "X_test = test.iloc[:,1:]\n",
    "y_test = test.iloc[:,0]\n",
    "\n",
    "#Scale the data\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1923331f",
   "metadata": {},
   "source": [
    "## Classifier comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f7df056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Nearest Neighbors .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.55      0.53        31\n",
      "           1       0.52      0.48      0.50        31\n",
      "\n",
      "    accuracy                           0.52        62\n",
      "   macro avg       0.52      0.52      0.52        62\n",
      "weighted avg       0.52      0.52      0.52        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Linear SVM .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.81      0.68        31\n",
      "           1       0.68      0.42      0.52        31\n",
      "\n",
      "    accuracy                           0.61        62\n",
      "   macro avg       0.63      0.61      0.60        62\n",
      "weighted avg       0.63      0.61      0.60        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for RBF SVM .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.40      0.13      0.20        31\n",
      "           1       0.48      0.81      0.60        31\n",
      "\n",
      "    accuracy                           0.47        62\n",
      "   macro avg       0.44      0.47      0.40        62\n",
      "weighted avg       0.44      0.47      0.40        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Decision Tree .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.61      0.62        31\n",
      "           1       0.62      0.65      0.63        31\n",
      "\n",
      "    accuracy                           0.63        62\n",
      "   macro avg       0.63      0.63      0.63        62\n",
      "weighted avg       0.63      0.63      0.63        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Random Forest .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.48      0.53        31\n",
      "           1       0.56      0.65      0.60        31\n",
      "\n",
      "    accuracy                           0.56        62\n",
      "   macro avg       0.57      0.56      0.56        62\n",
      "weighted avg       0.57      0.56      0.56        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Neural Net .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.45      0.48        31\n",
      "           1       0.51      0.58      0.55        31\n",
      "\n",
      "    accuracy                           0.52        62\n",
      "   macro avg       0.52      0.52      0.51        62\n",
      "weighted avg       0.52      0.52      0.51        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for AdaBoost .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.45      0.53        31\n",
      "           1       0.57      0.74      0.65        31\n",
      "\n",
      "    accuracy                           0.60        62\n",
      "   macro avg       0.61      0.60      0.59        62\n",
      "weighted avg       0.61      0.60      0.59        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Naive Bayes .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.45      0.53        31\n",
      "           1       0.57      0.74      0.65        31\n",
      "\n",
      "    accuracy                           0.60        62\n",
      "   macro avg       0.61      0.60      0.59        62\n",
      "weighted avg       0.61      0.60      0.59        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|       Classifier        |          Score          |\n",
      "------------------------------------------------------\n",
      "|    Nearest Neighbors    |   0.5161290322580645    |\n",
      "|       Linear SVM        |   0.6129032258064516    |\n",
      "|         RBF SVM         |   0.46774193548387094   |\n",
      "|      Decision Tree      |   0.6290322580645161    |\n",
      "|      Random Forest      |   0.5645161290322581    |\n",
      "|       Neural Net        |   0.5161290322580645    |\n",
      "|        AdaBoost         |   0.5967741935483871    |\n",
      "|       Naive Bayes       |   0.5967741935483871    |\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "names = [\n",
    "    \"Nearest Neighbors\",\n",
    "    \"Linear SVM\",\n",
    "    \"RBF SVM\",\n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\",\n",
    "    \"Neural Net\",\n",
    "    \"AdaBoost\",\n",
    "    \"Naive Bayes\",\n",
    "]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "]\n",
    "alllabels = ['Male', 'Female']\n",
    "scores = []\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    print('\\n........ Score and Classification Report for {0} .............\\n'.format(name))\n",
    "    scores.append(score)\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "    cfm1 = confusion_matrix(y_test, y_pred)\n",
    "    seaborn.heatmap(cfm1, xticklabels=alllabels, yticklabels=alllabels,cmap = 'YlGnBu')\n",
    "    plt.title('Confusion matrix', fontsize = 15)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "print(\"|{0:^25}|{1:^25}|\".format(\"Classifier\",\"Score\"))\n",
    "print(\"------------------------------------------------------\")\n",
    "for name,score in zip(names,scores):\n",
    "    print(\"|{0:^25}|{1:^25}|\".format(name,score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e19a20",
   "metadata": {},
   "source": [
    "# Classification Diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca3ef6e8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diseasefile_csv = os.path.join(data_path, 'TabelaDis.csv')\n",
    "df_dis = pd.read_csv(diseasefile_csv)\n",
    "df_dis.columns = df_dis.columns.str.replace(\"Average of \", \"\")\n",
    "df_dis.drop('ID', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f072954",
   "metadata": {},
   "source": [
    "Classification on disease features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8af84a8e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "train, test = train_test_split(df_dis, test_size=0.5)\n",
    "\n",
    "X_train = train.iloc[:,1:]\n",
    "y_train = train.iloc[:,0]\n",
    "X_test = test.iloc[:,1:]\n",
    "y_test = test.iloc[:,0]\n",
    "\n",
    "#Scale the data\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df8c2c9",
   "metadata": {},
   "source": [
    "## Classifier comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2120d358",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Nearest Neighbors .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      1.00      0.44         2\n",
      "           1       0.18      0.50      0.27         8\n",
      "           3       0.00      0.00      0.00         7\n",
      "           4       0.00      0.00      0.00         1\n",
      "\n",
      "   micro avg       0.19      0.33      0.24        18\n",
      "   macro avg       0.12      0.38      0.18        18\n",
      "weighted avg       0.11      0.33      0.17        18\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Linear SVM .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.26      1.00      0.41         8\n",
      "\n",
      "   micro avg       0.25      0.80      0.38        10\n",
      "   macro avg       0.13      0.50      0.21        10\n",
      "weighted avg       0.21      0.80      0.33        10\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for RBF SVM .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.21      0.75      0.33         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "           3       0.00      0.00      0.00         7\n",
      "\n",
      "   micro avg       0.19      0.26      0.22        23\n",
      "   macro avg       0.05      0.19      0.08        23\n",
      "weighted avg       0.07      0.26      0.12        23\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Decision Tree .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.14      0.50      0.22         2\n",
      "           1       0.20      0.38      0.26         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "           3       0.00      0.00      0.00         7\n",
      "           4       0.00      0.00      0.00         1\n",
      "           6       0.00      0.00      0.00         3\n",
      "\n",
      "   micro avg       0.12      0.15      0.14        27\n",
      "   macro avg       0.06      0.15      0.08        27\n",
      "weighted avg       0.07      0.15      0.09        27\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Random Forest .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      1.00      0.40         2\n",
      "           1       0.24      0.50      0.32         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "           3       0.00      0.00      0.00         7\n",
      "           4       0.00      0.00      0.00         1\n",
      "           6       1.00      0.33      0.50         3\n",
      "\n",
      "   micro avg       0.22      0.26      0.24        27\n",
      "   macro avg       0.25      0.31      0.20        27\n",
      "weighted avg       0.20      0.26      0.18        27\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Neural Net .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      1.00      0.44         2\n",
      "           1       0.22      0.50      0.31         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "           3       0.20      0.14      0.17         7\n",
      "           6       0.00      0.00      0.00         3\n",
      "\n",
      "   micro avg       0.22      0.27      0.24        26\n",
      "   macro avg       0.14      0.33      0.18        26\n",
      "weighted avg       0.14      0.27      0.17        26\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for AdaBoost .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.23      0.62      0.33         8\n",
      "           2       0.22      0.33      0.27         6\n",
      "           6       0.00      0.00      0.00         3\n",
      "\n",
      "   micro avg       0.22      0.41      0.29        17\n",
      "   macro avg       0.15      0.32      0.20        17\n",
      "weighted avg       0.19      0.41      0.25        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........ Score and Classification Report for Naive Bayes .............\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      1.00      0.44         2\n",
      "           1       0.29      0.62      0.40         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "           3       0.25      0.14      0.18         7\n",
      "           6       1.00      0.33      0.50         3\n",
      "\n",
      "   micro avg       0.28      0.35      0.31        26\n",
      "   macro avg       0.37      0.42      0.31        26\n",
      "weighted avg       0.30      0.35      0.26        26\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|       Classifier        |          Score          |\n",
      "------------------------------------------------------\n",
      "|    Nearest Neighbors    |         0.1875          |\n",
      "|       Linear SVM        |          0.25           |\n",
      "|         RBF SVM         |         0.1875          |\n",
      "|      Decision Tree      |          0.125          |\n",
      "|      Random Forest      |         0.21875         |\n",
      "|       Neural Net        |         0.21875         |\n",
      "|        AdaBoost         |         0.21875         |\n",
      "|       Naive Bayes       |         0.28125         |\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "names = [\n",
    "    \"Nearest Neighbors\",\n",
    "    \"Linear SVM\",\n",
    "    \"RBF SVM\",\n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\",\n",
    "    \"Neural Net\",\n",
    "    \"AdaBoost\",\n",
    "    \"Naive Bayes\",\n",
    "]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "]\n",
    "alllabels = ['allergies', 'hypertension', 'cholesterol', 'diabetes', 'arrhythmia', 'asthma', 'heart problems', 'brain problems']\n",
    "\n",
    "scores = []\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    print('\\n........ Score and Classification Report for {0} .............\\n'.format(name))\n",
    "    scores.append(score)\n",
    "    print(classification_report(y_test, y_pred, labels=np.unique(y_pred)))\n",
    "\n",
    "    cfm1 = confusion_matrix(y_test, y_pred)\n",
    "    seaborn.heatmap(cfm1, xticklabels=alllabels, yticklabels=alllabels,cmap = 'YlGnBu')\n",
    "    plt.title('Confusion matrix', fontsize = 15)\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "print(\"|{0:^25}|{1:^25}|\".format(\"Classifier\",\"Score\"))\n",
    "print(\"------------------------------------------------------\")\n",
    "for name,score in zip(names,scores):\n",
    "    print(\"|{0:^25}|{1:^25}|\".format(name,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e6ecf91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
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 "nbformat_minor": 5
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